Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Wednesday 10 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 140,084 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 140,074 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 61
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 30
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 24
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 16
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 15
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 13
## 95 2020-06-03 East of England 9
## 96 2020-06-04 East of England 5
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 4
## 99 2020-06-07 East of England 6
## 100 2020-06-08 East of England 2
## 101 2020-03-01 London 0
## 102 2020-03-02 London 0
## 103 2020-03-03 London 0
## 104 2020-03-04 London 0
## 105 2020-03-05 London 0
## 106 2020-03-06 London 1
## 107 2020-03-07 London 1
## 108 2020-03-08 London 0
## 109 2020-03-09 London 1
## 110 2020-03-10 London 0
## 111 2020-03-11 London 7
## 112 2020-03-12 London 6
## 113 2020-03-13 London 10
## 114 2020-03-14 London 14
## 115 2020-03-15 London 10
## 116 2020-03-16 London 17
## 117 2020-03-17 London 25
## 118 2020-03-18 London 31
## 119 2020-03-19 London 25
## 120 2020-03-20 London 45
## 121 2020-03-21 London 50
## 122 2020-03-22 London 54
## 123 2020-03-23 London 64
## 124 2020-03-24 London 87
## 125 2020-03-25 London 113
## 126 2020-03-26 London 130
## 127 2020-03-27 London 130
## 128 2020-03-28 London 122
## 129 2020-03-29 London 147
## 130 2020-03-30 London 150
## 131 2020-03-31 London 181
## 132 2020-04-01 London 202
## 133 2020-04-02 London 190
## 134 2020-04-03 London 196
## 135 2020-04-04 London 230
## 136 2020-04-05 London 195
## 137 2020-04-06 London 198
## 138 2020-04-07 London 219
## 139 2020-04-08 London 238
## 140 2020-04-09 London 206
## 141 2020-04-10 London 170
## 142 2020-04-11 London 176
## 143 2020-04-12 London 158
## 144 2020-04-13 London 166
## 145 2020-04-14 London 144
## 146 2020-04-15 London 142
## 147 2020-04-16 London 139
## 148 2020-04-17 London 100
## 149 2020-04-18 London 101
## 150 2020-04-19 London 102
## 151 2020-04-20 London 95
## 152 2020-04-21 London 95
## 153 2020-04-22 London 108
## 154 2020-04-23 London 77
## 155 2020-04-24 London 71
## 156 2020-04-25 London 57
## 157 2020-04-26 London 53
## 158 2020-04-27 London 51
## 159 2020-04-28 London 43
## 160 2020-04-29 London 44
## 161 2020-04-30 London 39
## 162 2020-05-01 London 41
## 163 2020-05-02 London 40
## 164 2020-05-03 London 36
## 165 2020-05-04 London 29
## 166 2020-05-05 London 25
## 167 2020-05-06 London 37
## 168 2020-05-07 London 37
## 169 2020-05-08 London 29
## 170 2020-05-09 London 23
## 171 2020-05-10 London 26
## 172 2020-05-11 London 18
## 173 2020-05-12 London 18
## 174 2020-05-13 London 16
## 175 2020-05-14 London 20
## 176 2020-05-15 London 18
## 177 2020-05-16 London 14
## 178 2020-05-17 London 15
## 179 2020-05-18 London 9
## 180 2020-05-19 London 13
## 181 2020-05-20 London 19
## 182 2020-05-21 London 12
## 183 2020-05-22 London 10
## 184 2020-05-23 London 6
## 185 2020-05-24 London 7
## 186 2020-05-25 London 9
## 187 2020-05-26 London 12
## 188 2020-05-27 London 7
## 189 2020-05-28 London 8
## 190 2020-05-29 London 7
## 191 2020-05-30 London 12
## 192 2020-05-31 London 6
## 193 2020-06-01 London 9
## 194 2020-06-02 London 7
## 195 2020-06-03 London 5
## 196 2020-06-04 London 8
## 197 2020-06-05 London 3
## 198 2020-06-06 London 0
## 199 2020-06-07 London 4
## 200 2020-06-08 London 2
## 201 2020-03-01 Midlands 0
## 202 2020-03-02 Midlands 0
## 203 2020-03-03 Midlands 1
## 204 2020-03-04 Midlands 0
## 205 2020-03-05 Midlands 0
## 206 2020-03-06 Midlands 0
## 207 2020-03-07 Midlands 0
## 208 2020-03-08 Midlands 3
## 209 2020-03-09 Midlands 1
## 210 2020-03-10 Midlands 0
## 211 2020-03-11 Midlands 2
## 212 2020-03-12 Midlands 6
## 213 2020-03-13 Midlands 5
## 214 2020-03-14 Midlands 4
## 215 2020-03-15 Midlands 5
## 216 2020-03-16 Midlands 11
## 217 2020-03-17 Midlands 8
## 218 2020-03-18 Midlands 13
## 219 2020-03-19 Midlands 8
## 220 2020-03-20 Midlands 28
## 221 2020-03-21 Midlands 13
## 222 2020-03-22 Midlands 31
## 223 2020-03-23 Midlands 33
## 224 2020-03-24 Midlands 41
## 225 2020-03-25 Midlands 48
## 226 2020-03-26 Midlands 64
## 227 2020-03-27 Midlands 72
## 228 2020-03-28 Midlands 89
## 229 2020-03-29 Midlands 92
## 230 2020-03-30 Midlands 90
## 231 2020-03-31 Midlands 123
## 232 2020-04-01 Midlands 140
## 233 2020-04-02 Midlands 142
## 234 2020-04-03 Midlands 124
## 235 2020-04-04 Midlands 151
## 236 2020-04-05 Midlands 164
## 237 2020-04-06 Midlands 140
## 238 2020-04-07 Midlands 123
## 239 2020-04-08 Midlands 186
## 240 2020-04-09 Midlands 139
## 241 2020-04-10 Midlands 127
## 242 2020-04-11 Midlands 142
## 243 2020-04-12 Midlands 139
## 244 2020-04-13 Midlands 120
## 245 2020-04-14 Midlands 116
## 246 2020-04-15 Midlands 147
## 247 2020-04-16 Midlands 102
## 248 2020-04-17 Midlands 118
## 249 2020-04-18 Midlands 115
## 250 2020-04-19 Midlands 92
## 251 2020-04-20 Midlands 107
## 252 2020-04-21 Midlands 86
## 253 2020-04-22 Midlands 78
## 254 2020-04-23 Midlands 103
## 255 2020-04-24 Midlands 79
## 256 2020-04-25 Midlands 72
## 257 2020-04-26 Midlands 81
## 258 2020-04-27 Midlands 74
## 259 2020-04-28 Midlands 68
## 260 2020-04-29 Midlands 53
## 261 2020-04-30 Midlands 56
## 262 2020-05-01 Midlands 64
## 263 2020-05-02 Midlands 51
## 264 2020-05-03 Midlands 52
## 265 2020-05-04 Midlands 61
## 266 2020-05-05 Midlands 58
## 267 2020-05-06 Midlands 59
## 268 2020-05-07 Midlands 48
## 269 2020-05-08 Midlands 34
## 270 2020-05-09 Midlands 37
## 271 2020-05-10 Midlands 42
## 272 2020-05-11 Midlands 33
## 273 2020-05-12 Midlands 45
## 274 2020-05-13 Midlands 39
## 275 2020-05-14 Midlands 37
## 276 2020-05-15 Midlands 40
## 277 2020-05-16 Midlands 34
## 278 2020-05-17 Midlands 31
## 279 2020-05-18 Midlands 34
## 280 2020-05-19 Midlands 34
## 281 2020-05-20 Midlands 36
## 282 2020-05-21 Midlands 32
## 283 2020-05-22 Midlands 27
## 284 2020-05-23 Midlands 33
## 285 2020-05-24 Midlands 19
## 286 2020-05-25 Midlands 26
## 287 2020-05-26 Midlands 33
## 288 2020-05-27 Midlands 29
## 289 2020-05-28 Midlands 27
## 290 2020-05-29 Midlands 20
## 291 2020-05-30 Midlands 20
## 292 2020-05-31 Midlands 20
## 293 2020-06-01 Midlands 20
## 294 2020-06-02 Midlands 20
## 295 2020-06-03 Midlands 23
## 296 2020-06-04 Midlands 13
## 297 2020-06-05 Midlands 19
## 298 2020-06-06 Midlands 13
## 299 2020-06-07 Midlands 9
## 300 2020-06-08 Midlands 2
## 301 2020-03-01 North East and Yorkshire 0
## 302 2020-03-02 North East and Yorkshire 0
## 303 2020-03-03 North East and Yorkshire 0
## 304 2020-03-04 North East and Yorkshire 0
## 305 2020-03-05 North East and Yorkshire 0
## 306 2020-03-06 North East and Yorkshire 0
## 307 2020-03-07 North East and Yorkshire 0
## 308 2020-03-08 North East and Yorkshire 0
## 309 2020-03-09 North East and Yorkshire 0
## 310 2020-03-10 North East and Yorkshire 0
## 311 2020-03-11 North East and Yorkshire 0
## 312 2020-03-12 North East and Yorkshire 0
## 313 2020-03-13 North East and Yorkshire 0
## 314 2020-03-14 North East and Yorkshire 0
## 315 2020-03-15 North East and Yorkshire 2
## 316 2020-03-16 North East and Yorkshire 3
## 317 2020-03-17 North East and Yorkshire 1
## 318 2020-03-18 North East and Yorkshire 2
## 319 2020-03-19 North East and Yorkshire 6
## 320 2020-03-20 North East and Yorkshire 5
## 321 2020-03-21 North East and Yorkshire 6
## 322 2020-03-22 North East and Yorkshire 7
## 323 2020-03-23 North East and Yorkshire 9
## 324 2020-03-24 North East and Yorkshire 8
## 325 2020-03-25 North East and Yorkshire 18
## 326 2020-03-26 North East and Yorkshire 21
## 327 2020-03-27 North East and Yorkshire 28
## 328 2020-03-28 North East and Yorkshire 35
## 329 2020-03-29 North East and Yorkshire 38
## 330 2020-03-30 North East and Yorkshire 64
## 331 2020-03-31 North East and Yorkshire 60
## 332 2020-04-01 North East and Yorkshire 67
## 333 2020-04-02 North East and Yorkshire 74
## 334 2020-04-03 North East and Yorkshire 100
## 335 2020-04-04 North East and Yorkshire 105
## 336 2020-04-05 North East and Yorkshire 92
## 337 2020-04-06 North East and Yorkshire 96
## 338 2020-04-07 North East and Yorkshire 102
## 339 2020-04-08 North East and Yorkshire 107
## 340 2020-04-09 North East and Yorkshire 111
## 341 2020-04-10 North East and Yorkshire 117
## 342 2020-04-11 North East and Yorkshire 98
## 343 2020-04-12 North East and Yorkshire 84
## 344 2020-04-13 North East and Yorkshire 94
## 345 2020-04-14 North East and Yorkshire 107
## 346 2020-04-15 North East and Yorkshire 96
## 347 2020-04-16 North East and Yorkshire 103
## 348 2020-04-17 North East and Yorkshire 88
## 349 2020-04-18 North East and Yorkshire 95
## 350 2020-04-19 North East and Yorkshire 88
## 351 2020-04-20 North East and Yorkshire 100
## 352 2020-04-21 North East and Yorkshire 76
## 353 2020-04-22 North East and Yorkshire 84
## 354 2020-04-23 North East and Yorkshire 63
## 355 2020-04-24 North East and Yorkshire 72
## 356 2020-04-25 North East and Yorkshire 69
## 357 2020-04-26 North East and Yorkshire 65
## 358 2020-04-27 North East and Yorkshire 65
## 359 2020-04-28 North East and Yorkshire 57
## 360 2020-04-29 North East and Yorkshire 69
## 361 2020-04-30 North East and Yorkshire 57
## 362 2020-05-01 North East and Yorkshire 64
## 363 2020-05-02 North East and Yorkshire 48
## 364 2020-05-03 North East and Yorkshire 40
## 365 2020-05-04 North East and Yorkshire 49
## 366 2020-05-05 North East and Yorkshire 40
## 367 2020-05-06 North East and Yorkshire 50
## 368 2020-05-07 North East and Yorkshire 45
## 369 2020-05-08 North East and Yorkshire 42
## 370 2020-05-09 North East and Yorkshire 44
## 371 2020-05-10 North East and Yorkshire 40
## 372 2020-05-11 North East and Yorkshire 29
## 373 2020-05-12 North East and Yorkshire 27
## 374 2020-05-13 North East and Yorkshire 28
## 375 2020-05-14 North East and Yorkshire 30
## 376 2020-05-15 North East and Yorkshire 32
## 377 2020-05-16 North East and Yorkshire 35
## 378 2020-05-17 North East and Yorkshire 26
## 379 2020-05-18 North East and Yorkshire 29
## 380 2020-05-19 North East and Yorkshire 27
## 381 2020-05-20 North East and Yorkshire 21
## 382 2020-05-21 North East and Yorkshire 33
## 383 2020-05-22 North East and Yorkshire 22
## 384 2020-05-23 North East and Yorkshire 18
## 385 2020-05-24 North East and Yorkshire 24
## 386 2020-05-25 North East and Yorkshire 21
## 387 2020-05-26 North East and Yorkshire 21
## 388 2020-05-27 North East and Yorkshire 20
## 389 2020-05-28 North East and Yorkshire 19
## 390 2020-05-29 North East and Yorkshire 24
## 391 2020-05-30 North East and Yorkshire 20
## 392 2020-05-31 North East and Yorkshire 18
## 393 2020-06-01 North East and Yorkshire 16
## 394 2020-06-02 North East and Yorkshire 22
## 395 2020-06-03 North East and Yorkshire 21
## 396 2020-06-04 North East and Yorkshire 17
## 397 2020-06-05 North East and Yorkshire 15
## 398 2020-06-06 North East and Yorkshire 20
## 399 2020-06-07 North East and Yorkshire 9
## 400 2020-06-08 North East and Yorkshire 7
## 401 2020-03-01 North West 0
## 402 2020-03-02 North West 0
## 403 2020-03-03 North West 0
## 404 2020-03-04 North West 0
## 405 2020-03-05 North West 1
## 406 2020-03-06 North West 0
## 407 2020-03-07 North West 0
## 408 2020-03-08 North West 1
## 409 2020-03-09 North West 0
## 410 2020-03-10 North West 0
## 411 2020-03-11 North West 0
## 412 2020-03-12 North West 2
## 413 2020-03-13 North West 3
## 414 2020-03-14 North West 1
## 415 2020-03-15 North West 4
## 416 2020-03-16 North West 2
## 417 2020-03-17 North West 4
## 418 2020-03-18 North West 6
## 419 2020-03-19 North West 7
## 420 2020-03-20 North West 10
## 421 2020-03-21 North West 11
## 422 2020-03-22 North West 13
## 423 2020-03-23 North West 16
## 424 2020-03-24 North West 21
## 425 2020-03-25 North West 21
## 426 2020-03-26 North West 29
## 427 2020-03-27 North West 35
## 428 2020-03-28 North West 28
## 429 2020-03-29 North West 46
## 430 2020-03-30 North West 67
## 431 2020-03-31 North West 52
## 432 2020-04-01 North West 86
## 433 2020-04-02 North West 96
## 434 2020-04-03 North West 95
## 435 2020-04-04 North West 98
## 436 2020-04-05 North West 102
## 437 2020-04-06 North West 100
## 438 2020-04-07 North West 133
## 439 2020-04-08 North West 127
## 440 2020-04-09 North West 119
## 441 2020-04-10 North West 117
## 442 2020-04-11 North West 138
## 443 2020-04-12 North West 126
## 444 2020-04-13 North West 127
## 445 2020-04-14 North West 131
## 446 2020-04-15 North West 114
## 447 2020-04-16 North West 134
## 448 2020-04-17 North West 98
## 449 2020-04-18 North West 113
## 450 2020-04-19 North West 71
## 451 2020-04-20 North West 83
## 452 2020-04-21 North West 76
## 453 2020-04-22 North West 86
## 454 2020-04-23 North West 85
## 455 2020-04-24 North West 66
## 456 2020-04-25 North West 65
## 457 2020-04-26 North West 55
## 458 2020-04-27 North West 54
## 459 2020-04-28 North West 57
## 460 2020-04-29 North West 62
## 461 2020-04-30 North West 59
## 462 2020-05-01 North West 44
## 463 2020-05-02 North West 56
## 464 2020-05-03 North West 55
## 465 2020-05-04 North West 48
## 466 2020-05-05 North West 48
## 467 2020-05-06 North West 44
## 468 2020-05-07 North West 49
## 469 2020-05-08 North West 42
## 470 2020-05-09 North West 30
## 471 2020-05-10 North West 41
## 472 2020-05-11 North West 34
## 473 2020-05-12 North West 38
## 474 2020-05-13 North West 24
## 475 2020-05-14 North West 26
## 476 2020-05-15 North West 33
## 477 2020-05-16 North West 32
## 478 2020-05-17 North West 24
## 479 2020-05-18 North West 30
## 480 2020-05-19 North West 35
## 481 2020-05-20 North West 27
## 482 2020-05-21 North West 25
## 483 2020-05-22 North West 26
## 484 2020-05-23 North West 31
## 485 2020-05-24 North West 26
## 486 2020-05-25 North West 31
## 487 2020-05-26 North West 27
## 488 2020-05-27 North West 27
## 489 2020-05-28 North West 27
## 490 2020-05-29 North West 19
## 491 2020-05-30 North West 17
## 492 2020-05-31 North West 13
## 493 2020-06-01 North West 12
## 494 2020-06-02 North West 26
## 495 2020-06-03 North West 21
## 496 2020-06-04 North West 19
## 497 2020-06-05 North West 14
## 498 2020-06-06 North West 15
## 499 2020-06-07 North West 12
## 500 2020-06-08 North West 6
## 501 2020-03-01 South East 0
## 502 2020-03-02 South East 0
## 503 2020-03-03 South East 1
## 504 2020-03-04 South East 0
## 505 2020-03-05 South East 1
## 506 2020-03-06 South East 0
## 507 2020-03-07 South East 0
## 508 2020-03-08 South East 1
## 509 2020-03-09 South East 1
## 510 2020-03-10 South East 1
## 511 2020-03-11 South East 1
## 512 2020-03-12 South East 0
## 513 2020-03-13 South East 1
## 514 2020-03-14 South East 1
## 515 2020-03-15 South East 5
## 516 2020-03-16 South East 8
## 517 2020-03-17 South East 7
## 518 2020-03-18 South East 10
## 519 2020-03-19 South East 9
## 520 2020-03-20 South East 14
## 521 2020-03-21 South East 7
## 522 2020-03-22 South East 25
## 523 2020-03-23 South East 20
## 524 2020-03-24 South East 22
## 525 2020-03-25 South East 29
## 526 2020-03-26 South East 34
## 527 2020-03-27 South East 34
## 528 2020-03-28 South East 36
## 529 2020-03-29 South East 54
## 530 2020-03-30 South East 58
## 531 2020-03-31 South East 65
## 532 2020-04-01 South East 65
## 533 2020-04-02 South East 55
## 534 2020-04-03 South East 72
## 535 2020-04-04 South East 80
## 536 2020-04-05 South East 82
## 537 2020-04-06 South East 88
## 538 2020-04-07 South East 100
## 539 2020-04-08 South East 83
## 540 2020-04-09 South East 104
## 541 2020-04-10 South East 88
## 542 2020-04-11 South East 88
## 543 2020-04-12 South East 88
## 544 2020-04-13 South East 84
## 545 2020-04-14 South East 65
## 546 2020-04-15 South East 72
## 547 2020-04-16 South East 56
## 548 2020-04-17 South East 86
## 549 2020-04-18 South East 57
## 550 2020-04-19 South East 70
## 551 2020-04-20 South East 85
## 552 2020-04-21 South East 50
## 553 2020-04-22 South East 54
## 554 2020-04-23 South East 57
## 555 2020-04-24 South East 64
## 556 2020-04-25 South East 51
## 557 2020-04-26 South East 51
## 558 2020-04-27 South East 40
## 559 2020-04-28 South East 40
## 560 2020-04-29 South East 47
## 561 2020-04-30 South East 29
## 562 2020-05-01 South East 37
## 563 2020-05-02 South East 36
## 564 2020-05-03 South East 17
## 565 2020-05-04 South East 35
## 566 2020-05-05 South East 29
## 567 2020-05-06 South East 25
## 568 2020-05-07 South East 27
## 569 2020-05-08 South East 26
## 570 2020-05-09 South East 28
## 571 2020-05-10 South East 19
## 572 2020-05-11 South East 25
## 573 2020-05-12 South East 27
## 574 2020-05-13 South East 18
## 575 2020-05-14 South East 32
## 576 2020-05-15 South East 24
## 577 2020-05-16 South East 22
## 578 2020-05-17 South East 18
## 579 2020-05-18 South East 22
## 580 2020-05-19 South East 12
## 581 2020-05-20 South East 22
## 582 2020-05-21 South East 14
## 583 2020-05-22 South East 17
## 584 2020-05-23 South East 21
## 585 2020-05-24 South East 16
## 586 2020-05-25 South East 13
## 587 2020-05-26 South East 18
## 588 2020-05-27 South East 17
## 589 2020-05-28 South East 12
## 590 2020-05-29 South East 17
## 591 2020-05-30 South East 8
## 592 2020-05-31 South East 10
## 593 2020-06-01 South East 11
## 594 2020-06-02 South East 9
## 595 2020-06-03 South East 17
## 596 2020-06-04 South East 9
## 597 2020-06-05 South East 9
## 598 2020-06-06 South East 4
## 599 2020-06-07 South East 4
## 600 2020-06-08 South East 1
## 601 2020-03-01 South West 0
## 602 2020-03-02 South West 0
## 603 2020-03-03 South West 0
## 604 2020-03-04 South West 0
## 605 2020-03-05 South West 0
## 606 2020-03-06 South West 0
## 607 2020-03-07 South West 0
## 608 2020-03-08 South West 0
## 609 2020-03-09 South West 0
## 610 2020-03-10 South West 0
## 611 2020-03-11 South West 1
## 612 2020-03-12 South West 0
## 613 2020-03-13 South West 0
## 614 2020-03-14 South West 1
## 615 2020-03-15 South West 0
## 616 2020-03-16 South West 0
## 617 2020-03-17 South West 2
## 618 2020-03-18 South West 2
## 619 2020-03-19 South West 5
## 620 2020-03-20 South West 3
## 621 2020-03-21 South West 6
## 622 2020-03-22 South West 9
## 623 2020-03-23 South West 9
## 624 2020-03-24 South West 7
## 625 2020-03-25 South West 9
## 626 2020-03-26 South West 11
## 627 2020-03-27 South West 13
## 628 2020-03-28 South West 21
## 629 2020-03-29 South West 18
## 630 2020-03-30 South West 23
## 631 2020-03-31 South West 23
## 632 2020-04-01 South West 22
## 633 2020-04-02 South West 23
## 634 2020-04-03 South West 30
## 635 2020-04-04 South West 42
## 636 2020-04-05 South West 32
## 637 2020-04-06 South West 34
## 638 2020-04-07 South West 39
## 639 2020-04-08 South West 47
## 640 2020-04-09 South West 24
## 641 2020-04-10 South West 46
## 642 2020-04-11 South West 43
## 643 2020-04-12 South West 23
## 644 2020-04-13 South West 27
## 645 2020-04-14 South West 24
## 646 2020-04-15 South West 32
## 647 2020-04-16 South West 29
## 648 2020-04-17 South West 33
## 649 2020-04-18 South West 25
## 650 2020-04-19 South West 31
## 651 2020-04-20 South West 26
## 652 2020-04-21 South West 26
## 653 2020-04-22 South West 22
## 654 2020-04-23 South West 17
## 655 2020-04-24 South West 19
## 656 2020-04-25 South West 15
## 657 2020-04-26 South West 27
## 658 2020-04-27 South West 13
## 659 2020-04-28 South West 17
## 660 2020-04-29 South West 15
## 661 2020-04-30 South West 26
## 662 2020-05-01 South West 6
## 663 2020-05-02 South West 7
## 664 2020-05-03 South West 10
## 665 2020-05-04 South West 16
## 666 2020-05-05 South West 14
## 667 2020-05-06 South West 19
## 668 2020-05-07 South West 16
## 669 2020-05-08 South West 6
## 670 2020-05-09 South West 11
## 671 2020-05-10 South West 5
## 672 2020-05-11 South West 8
## 673 2020-05-12 South West 7
## 674 2020-05-13 South West 7
## 675 2020-05-14 South West 6
## 676 2020-05-15 South West 4
## 677 2020-05-16 South West 4
## 678 2020-05-17 South West 6
## 679 2020-05-18 South West 4
## 680 2020-05-19 South West 6
## 681 2020-05-20 South West 1
## 682 2020-05-21 South West 9
## 683 2020-05-22 South West 6
## 684 2020-05-23 South West 6
## 685 2020-05-24 South West 3
## 686 2020-05-25 South West 8
## 687 2020-05-26 South West 11
## 688 2020-05-27 South West 5
## 689 2020-05-28 South West 9
## 690 2020-05-29 South West 4
## 691 2020-05-30 South West 3
## 692 2020-05-31 South West 2
## 693 2020-06-01 South West 6
## 694 2020-06-02 South West 2
## 695 2020-06-03 South West 5
## 696 2020-06-04 South West 1
## 697 2020-06-05 South West 1
## 698 2020-06-06 South West 1
## 699 2020-06-07 South West 2
## 700 2020-06-08 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Tuesday 09 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.9476 -1.9785 -0.0241 1.8768 4.5458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.094e+00 5.091e-02 100.06 <2e-16 ***
## note_lag 1.043e-05 4.905e-07 21.27 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.751975)
##
## Null deviance: 3808.89 on 38 degrees of freedom
## Residual deviance: 294.85 on 37 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 163.08688 1.00001
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 147.454301 180.028825
## note_lag 1.000009 1.000011
Rsq(lag_mod)
## [1] 0.9225903
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.4.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.5.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.2
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0